As we move further towards an open data economy, public investment in developing and maintaining the open data ecosystem will be critical to its prosperity. However, resource allocation often depends on the return on investment as well as who the beneficiaries are. This ultimately relies on revealing the economic and social value of open data and identifying the industries impacted.

Our human need to estimate this value has led to the consideration of user-centric metrics, which may provide a framework against which open data may be assessed. Though measures and metrics are simply constructs in an attempt to quantify the state of open data today, good metrics evolve to keep pace with the evolution of our digital ecosystem and may be broader than simply considering the user perspective. In any case open data metrics are just a proxy for determining the characteristics of the data, of which the inherent value is of great interest.

Now, it may seem prudent to consider the usefulness of datasets in determining the value of the data, but this is largely dependent on the applications envisioned for the data. And what’s more, this is innately difficult to quantify as it depends on how innovatively the data is used, and also on what the consumer demand for the data is.

The concept of ‘data usability’, however, is less abstract and focuses more on our current technological resources in ascertaining the potential for data consumption. The Open Data Usability Index (ODUI), as defined by Phoensight, examines the accessibility, quantity, quality and openness of open data using a data-driven approach so as to determine the readiness of this information for public and industry consumption.

A detailed analysis of the ODUI by industry, has lead the open data community to question whether the responsibility for the release of open data lies predominantly with governments, and to suggest there is an economic case for specific industries to support the release of open data that may be of direct relevance to them.

Governments may be better placed to provide the digital infrastructure needed, but it is unclear whether industry should be incentivized to support these efforts to maintain a thriving open data ecosystem, given that they stand to benefit significantly from open data releases. Accurately quantifying stakeholder impacts may be crucial to the evolution of open digital markets and could change their dynamics, resulting in more responsive and efficient interactions, ultimately benefiting consumers.

Machine Learning approaches have the advantage of identifying various open data taxonomies, which may be used to derive new insights into the open data market. Futuristic open data platforms, sophisticated enough to include Artificial Intelligence (AI) may one day be trained to identify and suggest datasets to be employed for targeted needs or to answer specific questions. Our current continuum of data processing—from data provider to end user—may itself be compressed by smart technology for information consumption.

What will become increasingly valuable in integrating technology with our decision-making will be intangible qualities such as ‘user confidence in technology’ and ‘public trust’, which may encompass differences in intergenerational attitudes to global digitalization.

In this new paradigm of real-time data, highly connected networks, smart apps, the democratization of technology and the packaging of complex information for tacit knowledge assimilation, the distinction between data providers, infomediaries, users and consumers will no longer prove beneficial in open data market analysis.

In such a world, understanding the open data market dynamics will focus more on the nature of the goods themselves, how they are sustained and how they are consumed. If open data is to be truly embedded in social and economic well-being, and in future prosperity, it will need to seamlessly integrate with market and social interactions.

Dr. Audrey Lobo-Pulo is a Senior Adviser in the Australian Public Service, and is an advocate for open government and open source software in government modelling. Views expressed in this article are those of the author and not necessarily those of the Australian Government, nor of the Global Digital Foundation, which does not hold corporate views.